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Framelet-based algorithm for segmentation of tubular structures

Framelet-based algorithm for segmentation of tubular structures
Framelet-based algorithm for segmentation of tubular structures
Framelets have been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the partial differential equation modeling. In this paper, we apply the framelet-based approach to identify tube-like structures such as blood vessels in medical images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the framelet-based algorithm to denoise and smooth the possible boundary and sharpen the region. Numerical experiments of real 2D/3D images demonstrate that the proposed method is very efficient and outperforms other existing methods. © 2012 Springer-Verlag.
411-422
SpringerLink
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond H.
1898019c-f54f-4b64-807d-d1335e76fd38
Morigi, Serena
6ec66b6d-8e27-43bf-84ec-c5069f73e8a5
Sgallari, Fiorella
31472b49-c051-4ee0-ac56-915034c4af74
Bruckstein, Alfred M.
Haar Romeny, Bart M. ter
Bronstein, Alexander M.
Bronstein, Michael M.
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond H.
1898019c-f54f-4b64-807d-d1335e76fd38
Morigi, Serena
6ec66b6d-8e27-43bf-84ec-c5069f73e8a5
Sgallari, Fiorella
31472b49-c051-4ee0-ac56-915034c4af74
Bruckstein, Alfred M.
Haar Romeny, Bart M. ter
Bronstein, Alexander M.
Bronstein, Michael M.

Cai, Xiaohao, Chan, Raymond H., Morigi, Serena and Sgallari, Fiorella (2012) Framelet-based algorithm for segmentation of tubular structures. In, Bruckstein, Alfred M., Haar Romeny, Bart M. ter, Bronstein, Alexander M. and Bronstein, Michael M. (eds.) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), , (doi:10.1007/978-3-642-24785-9_35), 6667 LNCS) Third International Conference: Scale Space and Variational Methods in Computer Vision (29/05/11 - 02/06/11) SpringerLink, pp. 411-422. (doi:10.1007/978-3-642-24785-9_35).

Record type: Book Section

Abstract

Framelets have been used successfully in various problems in image processing, including inpainting, impulse noise removal, super-resolution image restoration, etc. Segmentation is the process of identifying object outlines within images. There are quite a few efficient algorithms for segmentation that depend on the partial differential equation modeling. In this paper, we apply the framelet-based approach to identify tube-like structures such as blood vessels in medical images. Our method iteratively refines a region that encloses the possible boundary or surface of the vessels. In each iteration, we apply the framelet-based algorithm to denoise and smooth the possible boundary and sharpen the region. Numerical experiments of real 2D/3D images demonstrate that the proposed method is very efficient and outperforms other existing methods. © 2012 Springer-Verlag.

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Published date: 2012
Venue - Dates: Third International Conference: Scale Space and Variational Methods in Computer Vision, Israel, 2011-05-29 - 2011-06-02

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Local EPrints ID: 438575
URI: http://eprints.soton.ac.uk/id/eprint/438575
PURE UUID: 408ce908-b2d0-4f81-aa05-97e7acc5a0b1

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Date deposited: 17 Mar 2020 17:33
Last modified: 19 May 2020 17:03

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Contributors

Author: Xiaohao Cai
Author: Raymond H. Chan
Author: Serena Morigi
Author: Fiorella Sgallari
Editor: Alfred M. Bruckstein
Editor: Bart M. ter Haar Romeny
Editor: Alexander M. Bronstein
Editor: Michael M. Bronstein

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